Quality of sleep in patients with chronic kidney disease
Bibliographic record
Abstract
BACKGROUND: Sleep disorders are common in patients with renal failure on dialysis; however, the prevalence of "poor sleep" in patients with chronic kidney disease (CKD) not yet on dialysis is not known. This study aimed to measure the prevalence of "poor sleep" in CKD patients and to examine the association between quality of sleep and the degree of renal impairment in this population. METHODS: Quality of sleep was measured using the Pittsburgh Sleep Quality Index (PSQI) in 120 prevalent CKD patients. RESULTS: Sixty-three subjects (53%) had "poor sleep" defined as a global PSQI score >5. There was no statistically significant relationship between the global PSQI score and the blood urea nitrogen level (BUN), serum creatinine level or calculated creatinine clearance, but the sleep efficiency component score correlated with BUN (r = 0.19, P = 0.04) and serum creatinine (r = 0.20, P = 0.03). A history of depression was the only independent predictor of "poor sleep" (global PSQI >5). CONCLUSIONS: "Poor sleep" is common in CKD patients. Quality of sleep decreases in the early stages of CKD and does not appear to be associated with the subsequent degree of renal failure. Large prospective longitudinal studies of quality of sleep in CKD patients are needed to confirm the high prevalence of impaired quality of sleep in this population and examine the association between renal function and quality of sleep while controlling for potential confounding variables.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".